MultiCloud Cluster comes to MongoDB

MultiCloud Cluster comes to MongoDB

MongoDB Atlas has been updated with support for multi-cloud clusters. The developers have also updated MongoDB CLI and say it now offers ‘Atlas performance advice on your terminal’.

MongoDB is a NoSQL document database that stores its documents in a JSON-like format with schema.  MongoDB Atlas is the fully managed global cloud version of the software that can be run on AWS, Azure, or Google Cloud.

The support for multi-cloud clusters on MongoDB Atlas means customers can distribute their data in a single cluster across multiple public clouds simultaneously, or move workloads seamlessly between them.

The MongoDB team says they’re seeing more organizations moving towards a multi-cloud model, partially so developers can choose the exact cloud service for analysis, such as AWS Lambda, Google Cloud AI Platform, and Azure Cognitive Services.

With multi-cloud clusters, developers can now run operational and analytical workloads using different cloud tools on the same dataset, with no manual data replication required. The multi-cloud support includes built-in automation that handles cross-cloud data replication on a rolling basis so applications stay online and available to end-users.

Multi-cloud clusters come with built-in security defaults, fully managed backup and restores, automated patches and upgrades, and intelligent performance advice. The team is planning on releasing more capabilities in the coming months. 

Alongside the support for multi-cloud clusters on MongoDB Atlas, the developers have improved MongoDB CLI to include commands to use features of MongoDB cloud (MongoDB Atlas, MongoDB Cloud Manager, and MongoDB Ops Manager) on local terminals, so developers can you write advanced scripts and automate different workflows for a MongoDB cloud infrastructure.

The latest release adds the ability to get Atlas performance recommendations. These are based on MongoDB Atlas  Performance Advisor, which works by scanning your Atlas Cluster logs and finding any slow operations that could be affecting the performance of your queries.

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